Our groups study dynamics and computation in neural circuits using a combination of approaches from dynamical systems, control theory, and statistical inference. The jointly led project will combine normative with bottom-up approaches to study the neural implementation of challenging computations, such as probabilistic inference or memory recall. Areas of interest include balanced network dynamics, synaptic plasticity, and Bayesian inference.

The successful candidate will have

a strong quantitative background

demonstrable interest in theoretical neuroscience

obtained (or be close to the completion of) a PhD or equivalent in computational neuroscience, physics, mathematics, computer science, machine learning or a related field

Preference will be given to candidates with

previous experience in computational neuroscience

sufficient programming skills to run numerical simulations (eg. in C or MatLab)

Postdoc: Memory (collaboration with Peter Dayan and Rik Henson)

We are seeking a highly creative and motivated postdoctoral fellow (research associate or senior research associate) in the group of Máté Lengyel in the Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, to work on circuit- and systems-level of memory, in particular the interaction between recollective and familiarity-based forms of recognition memory.

The project involves collaboration with Peter Dayan (Gatsby Computational Neuroscience Unit, UCL), and also Rik Henson (MRC Cognition and Brain Unit, Cambridge) providing direct access to relevant behavioural and imaging data. More broadly, the group studies learning and memory from computational, algorithmic/representational and neurobiological viewpoints. Computationally and algorithmically, we use ideas from Bayesian approaches to statistical inference and reinforcement learning to characterise the goals and mechanisms of learning in terms of normative principles and behavioral results. We also perform dynamical systems analyses of reduced biophysical models to understand the mapping of these mechanisms into cellular and network models. We collaborate very closely with experimental neuroscience groups, doing in vitro intracellular recordings, multi-unit recordings in behaving animals, human psychophysical, and fMRI experiments.

The successful candidate will have

a strong quantitative background

demonstrable interest in theoretical neuroscience

obtained (or be close to the completion of) a PhD or equivalent in computational neuroscience, physics, mathematics, computer science, machine learning or a related field

Preference will be given to candidates with

previous experience in computational neuroscience

sufficient programming skills to run numerical simulations (eg. in C or MatLab)